• Korean Journal of Heritage: History & Science
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• v.56 no.1
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• pp.118-130
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• 2023

Decay or large cavities inside trees are the main causes of trees overturning and broken branches, and structurally weakened trees are more vulnerable to strong winds and heavy snowfall. Recently, as strong winds and typhoons increase due to climate change, the damage to human life and property due to trees overturning continues to increase, and cultural assets are in a similar situation. In particular, old big trees are structurally vulnerable to external shocks such as strong winds and heavy snowfall. This study was aimed at providing a scientific basis for preventive protection measures by conducting a structural stability diagnosis of seven retusa fringe trees designated as natural monuments. For the structural stability diagnosis, tree risk assessment and internal tree defect measurements were performed. As a result of the tree risk assessment, the Retusa Fringe Trees in Sinjeon-ri, Yangsan and Gwangyangeupsu had the highest risk of broken branches due to weak branch attachment strength. As a result of the diagnosis of internal defects of cross sections of measured trees, there were suspected cavities or severe decay in all except two trees of the population of Retusa Fringe Trees in Pyeongji-ri. Natural disasters due to climate change are increasing, and the scale is getting larger, so it is very important to preemptively manage large old trees through scientific structural safety diagnosis to manage trees that are vulnerable to environmental changes.

• Journal of the Korean Institute of Landscape Architecture
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• v.41 no.6
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• pp.20-28
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• 2013

The purpose of this study was to compare the ultra-violet(UV) radiation under the landscaping shade facilities and tree with natural solar UV of the outdoor space at summer middays. The UVA+B and UVB were recorded every minute from the $20^{th}$ of June to the $26^{th}$ of September 2012 at a height of 1.1m above in the four different shading conditions, with fours same measuring system consisting of two couple of analog UVA+B sensor(220~370nm, Genicom's GUVA-T21GH) and UVB sensor(220~320nm, Genicom's GUVA-T21GH) and data acquisition systems(Comfile Tech.'s Moacon). Four different shading conditions were under an wooden shelter($W4.2m{\times}L4.2m{\times}H2.5m$), a polyester membrane structure ($W4.9m{\times}L4.9m{\times}H2.6m$), a Salix koreensis($H11{\times}B30$), and a brick-paved plot without any shading material. Based on the 648 records of 17 sunny days, the time serial difference of natural solar UVA+B and UVB for midday periods were analysed and compared, and statistical analysis about the difference between the four shading conditions was done based on the 2,052 records of daytime period from 10 A.M. to 4 P.M.. The major findings were as follows; 1. The average UVA+B under the wooden shelter, the membrane and the tree were $39{\mu}W/cm^2$(3.4%), $74{\mu}W/cm^2$(6.4%), $87{\mu}W/cm^2$(7.6%) respectively, while the solar UVA+B was $1.148{\mu}W/cm^2$. Which means those facilities and tree screened at least 93% of solar UV+B. 2. The average UVB under the wooden shelter, the membrane and the tree were $12{\mu}W/cm^2$(5.8%), $26{\mu}W/cm^2$(13%), $17{\mu}W/cm^2$(8.2%) respectively, while the solar UVB was $207{\mu}W/cm^2$. The membrane showed the highest level and the wooden shelter lowest. 3. According to the results of time serial analysis, the difference between the three shaded conditions around noon was very small, but the differences of early morning and late afternoon were apparently big. Which seems caused by the matter of the formal and structural characteristics of the shading facilities and tree, not by the shading materials itself. In summary, the performance of the four landscaping shade facilities and tree were very good at screening the solar UV at outdoor of summer middays, but poor at screening the lateral UV during early morning and late afternoon. Therefore, it can be apparently said that the more delicate design of shading facilities and big tree or forest to block the additional lateral UV, the more effective in conditioning the outdoor space reducing the useless or even harmful radiation for human activities.

• Korean Journal of Heritage: History & Science
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• v.42 no.1
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• pp.144-157
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• 2009

Nondestructive methods to check the vitality of trees and to find out internal decay of old-giant trees include the use of electrical resistance, ultrasound transmission time, microdrilling, and infrared thermography etc. Among these, ultrasound transmission offers some advantages compared to others such as it is an entirely nondestructive detection method and it can be applied to very big trees. However, the ultrasound equipment is comparatively expensive and not broadly spread yet. On the other hand, Shigometer is versatile to be applied to check vitality of the tree and find out internal decay. Electrical conductivity of plant tissues is a very useful characteristics to determine the vitality and internal decay of trees. Electrical resistance of cambial area tells about the vitality of a tree and electrical resistance of heartwood reveals discoloration or decay of it. For determination of the vitality of the tree, the standard equation for calibration of measured electrical resistances should be developed by measuring and analyzing electrical resistance from at least 30-40 trees of the same species with that tree. All the factors, especially tree species, diameter of the stem, and temperature, which can altered the electrical resistance of trees, should be taken into consideration in the development of the equation. If the standard equation is developed for old-giant trees that we should conserve, it will be very useful. In addition, periodical and continued measuring of a certain tree will help to determine the condition of the tree by comparing the measurement with accumulated data of the tree. Measuring electrical resistance of wood might not require the standard equation. But it also needs to check electrical resistance of sound wood of the same tree species. If the stems that should be examined is thicker than 40cm, it is better to use the ultrasound measurement combined to Shigometer.

• Journal of the Korean Society of Safety
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• v.25 no.6
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• pp.210-215
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• 2010

In this study, when the forest fire occurred, in order to estimate greenhouse gas emissions, tree glow in Gangwon Youndong area, Juglans mandshurica, Alnus japonica, Acer palmatum of carbon dioxide and carbon monoxide emissions were about. Water content were measured before the experiment, Juglans mandshurica 196.24%, Alnus japonica 169.17% Acer palmatum 210.10% moisture content showed a big difference, Living leaves of Acer palmatum were a lot of moisture. Also, 50g weight of carbon dioxide on the Juglans mandshurica 53.3644g, Alnus japonica 49.4256g, was released about Acer palmatum 51.3394g, Juglans mandshurica living leaves were the most carbon dioxide emissions. Carbon monoxide emissions result, About weight 50g Juglans mandshurica 1.5329g, Alnus japonica 1.7189g, 2.5002g about Acer palmatum was released, Acer palmatum living leaves were the most carbon monoxide emissions.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.41-48
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• 2019

In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.275-279
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• 2022

The growth of technology nowadays has made many things easy for humans. These things are from everyday small task to more complex tasks. Such growth also comes with the illegal activities that are perform by using technology. These illegal activities can simple as displaying annoying message to big frauds. The easiest way for the attacker to perform such activities is to convenience user to click on the malicious link. It has been a great concern since a decay to classify URLs as malicious or benign. The blacklist has been used initially for that purpose and is it being used nowadays. It is efficient but has a drawback to update blacklist automatically. So, this method is replace by classification of URLs based on machine learning algorithms. In this paper we have use four machine learning classification algorithms to classify URLs as malicious or benign. These algorithms are support vector machine, random forest, n-nearest neighbor, and decision tree. The dataset that is used in this research has 36694 instances. A comparison of precision accuracy and recall values are shown for dataset with and without preprocessing.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.1-10
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• 2024

In the era of big data, the growth of e-commerce transactions brings forth both opportunities and risks, including the threat of data theft and fraud. To address these challenges, an automated real-time fraud detection system leveraging machine learning was developed. Four algorithms (Decision Tree, Naïve Bayes, XGBoost, and Neural Network) underwent comparison using a dataset from a clothing website that encompassed both legitimate and fraudulent transactions. The dataset exhibited an imbalance, with 9.3% representing fraud and 90.07% legitimate transactions. Performance evaluation metrics, including Recall, Precision, F1 Score, and AUC ROC, were employed to assess the effectiveness of each algorithm. XGBoost emerged as the top-performing model, achieving an impressive accuracy score of 95.85%. The proposed system proves to be a robust defense mechanism against fraudulent activities in e-commerce, thereby enhancing security and instilling trust in online transactions.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.105-112
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• 2021

In the era of big data, interest in data is exploding. In particular, the development of the Internet and social media has led to the creation of new data, enabling the realization of the era of big data and artificial intelligence and opening a new chapter in convergence technology. Also, in the past, there are many demands for analysis of data that could not be handled by programs. In this paper, an analysis model was designed and verified for classification of unstructured data, which is often required in the era of big data. Data crawled DBPia's thesis summary, main words, and sub-keyword, and created a database using KoNLP's data dictionary, and tokenized words through morpheme analysis. In addition, nouns were extracted using KAIST's 9 part-of-speech classification system, TF-IDF values were generated, and an analysis dataset was created by combining training data and Y values. Finally, The adequacy of classification was measured by applying three analysis algorithms(random forest, SVM, decision tree) to the generated analysis dataset. The classification model technique proposed in this paper can be usefully used in various fields such as civil complaint classification analysis and text-related analysis in addition to thesis classification.

• Journal of Korea Foresty Energy
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• v.21 no.2
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• pp.51-61
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• 2002

This study was conducted to investigate and compare species composition, stand structure, and growth pattern for two different sites in which silvopastoral system has been taking place. One site was the pasture area where a number of trees were removed and grasses were planted for cattle grazing, and the other site was the deciduous forest that has been established by ecological succession. The results were as follows: 1. Nine tree species were present equally in the pasture area and the deciduous forest. Of these species, seven tree species were growing in common for two sites. However, the species composition, including density and frequency, was varied by sites. 2. The number of stems per hectare in the pasture area was 71, and that in the deciduous forest was 1,433, having shown the big difference. It is estimated that, considering the growth rate, better grown trees were remained at the time of harvesting in 1996. 3. The growth of diameter, height, and basal area in the pasture area was superior to that in the nearby deciduous forest. In spite of higher values of diameter and height, the timber volume of pasture area per unit area was less than 15% of that in the deciduous forest. 4. Providing sufficient growing space, the pasture area supported higher values of diameter and height. The wider growing space also had influence on the expansion of crown of trees by the result of deliquescent growth pattern. From this point of view, more research would be needed to establish appropriate number of trees for silvopastoral system.